Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.

ABSTRACT: While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind

SUBMITTER: Liu Y 

PROVIDER: S-EPMC5808454 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature